sjchoi86/bayes-nn
Lecture notes on Bayesian deep learning
This content provides an academic overview and detailed explanations of Bayesian Deep Learning. It delves into foundational mathematical concepts, explores Gaussian processes, and covers advanced topics like variational inference and uncertainty estimation in deep neural networks. Researchers and graduate students in machine learning and artificial intelligence would find this useful for understanding the theoretical underpinnings and practical methods of Bayesian deep learning.
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Use this if you are a researcher or graduate student looking to understand the theoretical and mathematical foundations of Bayesian Deep Learning, including its application in estimating uncertainty.
Not ideal if you are looking for a plug-and-play code library or a high-level, non-technical introduction to deep learning concepts.
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Feb 11, 2018
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